For web designers today, creating a website can mean a whole lot than just functionality, usability and aesthetic appeal. Today, every new-born website requires a thorough integration of Search Engine Optimization (SEO) protocols to become crawlable and get indexed by search engines such as Google.

A good website can attract great amounts of traffic. However, to make sure your traffic is relevant, geo-specific, and hails from the target segment, you must utilize SEO properly. According to one piece of HubSpot research, 77% of people research a brand before getting in touch with it. This means your site design, structure, content, and marketing practices must be spot on if you want spectacular search results!

Both off-page and on-page SEO are imperative to the ranking process for any website on Google. Here, we are going to discuss why web designers should know about on-page SEO well enough to create a website that not only attracts visitors, but also ranks on top of Google search engine result pages (SERPs).

1. Higher Rankings

On-page SEO involves many elements such as HTTP status code, URLs and their friendliness with the search engine. Other aspects include the correct addition of meta tags, descriptions and further heading tags on your search link on Google SERPs. All of these elements make a huge difference in on-page SEO. Therefore, a web designer who knows these details must know when to apply them in the right order so that the website receives higher rankings on Google.

2. Greater Search Accuracy

With the growing number of internet users, the demand of the data has also increased. There are so many brands for a similar product, over hundreds of online stores, and numerous branches of the same brand. Before any potential customer makes an appearance in a store, they are highly likely to search them on the internet. The statistics clearly support this as 18% more shoppers prefer Google over Amazon for searching a product and 136% of times a search engine is preferred over other websites for the same purpose. Similarly, local searches lead 50% of the mobile users to take a tour to the nearby store within 24 hours. This further necessitates web designers to readily know about on-page SEO so that the client’s business page is more visible on web.

3. More Mobile Traffic

The state of inbound reporting suggests that generating traffic is one of the main marketing challenges faced by website designers and marketers. Website designers have the opportunity to integrate SEO metrics from the start and not only make the website more user-friendly, but device responsive as well. According to marketing technology facts by Sweor 57% of the mobile users abandon a brand’s website if it has a poor mobile responsive website. SEO helps you improve these flaws and add in high-quality visual content for better marketing. Designers can use this to their advantage and focus on building an attractive, rankable and responsive website.

4. Higher Engagement

In the present era, every online brand is reflection of how far up it is on Google rankings. On-page SEO helps build a strong network of internal linking that keeps the user engaged on the website by offering them more valuable information on the right time.

It also helps brings exposure to those sectors of the website that need more attention and helps generate a positive user experience from the visitor. This helps the brand focus on its goals and deploy different marketing strategies to boost revenues.

5. Impartial Benefits for SMEs

While large businesses may dominate the small ones in terms of size, operations and employee strength, SEO does not discriminate between SMEs and Large enterprises. SEO does not require a sizeable investment and most entrepreneurs and SMEs can afford hiring a few resources or even build their own department. However, SMEs with constrained budgets may not be able afford a dedicated department for SEO. Therefore, web designers must know SEO beforehand since there is no guarantee they will get any guidance from the company when the website gets live.

6. More Quality Traffic

Designing a website with proper on-page SEO helps Google’s spiders to crawl through your URLs faster and index your pages more relevantly on their SERPs. Research conducted by Moz suggests that 71.33% of clicks made on a website are present on the first page of search results. This means that more and quality traffic would be driven to your website generating more leads, increasing the conversion rates and ROI as well.

7. Using Innovative Technologies

Content has a direct effect on your customers. According MindMeld, 60% of the users have started using voice search features to interact with search engines when making queries. This means that the designers now need to optimize the website and content for voice search as well. According to Backlinko, the average word length that helps rank the website in the first page of Google is 1890 words. Also, the use of most suitable keywords gives your website ranking a boost bringing it on the first result page of the search engine. To get more advanced SEO features, web designers also deploy SEO extensions for more optimized performance and cost effectiveness.

8. Increases Page Loading Speed

Every website designer knows that loading speed plays a deciding role in online rankings as well as user experience. Some of the factors that lower the webpage speed are the large images, bad URLs and coding, and themes with too many widgets. Thus, knowing on-page SEO helps the designer avoid such errors when designing the website, improving its loading speeds far more efficiently as compared to when it is operational.

9. Greater User Experience

You must be wondering how SEO improves the UX, right? Well, good SEO offers informative, readable and highly usable content to the readers. Also, it helps to design a visually attractive website that is nicely navigated and performs well. These features make users happy and enhance their experience on the web page. So if you’re planning to leave a long lasting impression right from the start, you must put in some on-page SEO from the beginning.

10. Cost-Effectiveness

Its irrefutable that SEO has a great cost advantage. A skilled web designer knows how well systematic integration of on-page SEO can save costs that can pile up later if the website starts getting traffic. Everything from page titles, meta descriptions, meta tags, URL structure, body tags, keyword density down to image SEO must be prepared prior to its operation stage. Neglecting these key points can be detrimental to the website’s overall progress and may result on expensive retro-fitting at a later date.

Featured image via Unsplash

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from Webdesigner Depot https://www.webdesignerdepot.com/2019/02/know-seo-on-page-seo-10-real-world-reasons-designers-should-know-seo/

How They Influence Human Behavior Online

A large majority of marketers wouldn’t consider themselves psychologists. Yet understanding the growing field of marketing psychology can help persuade and influence audiences in powerful ways online.

Great campaigns happen at the intersection of marketing and psychology. The sweet spot where your content and messaging connects with your audience on a deep, human level.

Which, in a sense, makes all marketers aspiring psychologists at heart.

This week on The Science of Social Media, we’re exploring six powerful psychological biases and how they influence human behavior online. Knowing the factors that affect decisions will help take your social media marketing to the next level.

Let’s dive in!

6 Powerful Biases That Influence Human Behavior

• Psychological Bias 1: The Bandwagon Effect
• Psychological Bias 2: Zero Risk Bias or the Certainty Effect
• Psychological Bias 3: In-Group Favoritism
• Psychological Bias 4: Confirmation Bias
• Psychological Bias 5: The Endowment Effect
• Psychological Bias 6: Not Invented Here

What follows is a lightly-edited transcript of the Buffer Podcast episode #130 for your reading pleasure.

Hailley: Let kick off the show by quickly exploring how a psychological bias is defined. In this case, we’re talking about cognitive biases:

Cognitive and psychological biases are defined as repetitive paths that your mind takes when doing things like evaluating, judging, remembering, or making a decision.

Just like instincts, they evolved so that we don’t have to think as much for every decision that we make and they help us conserve energy.

Brian: More than knowing these for marketing, it’s also really interesting to be able to pinpoint your own cognitive biases to be aware of what goes into your decisions, judgments, and opinions.

Let’s get started looking into each of the biases we’ve identified and how they relate to marketing. And just a note that there are a lot of cognitive biases in our brains, these are just a select few that we found particularly useful for marketing but this is in no way a complete list.

1. The Bandwagon Effect

Hailley: Let’s start with one that most people have probably already heard of, it’s called the bandwagon effect. You’ve probably seen the expression “they jumped on the bandwagon” and that’s what this cognitive bias is referring to.

The idea is that the rate of uptake of beliefs, ideas, fads, and trends increases the more that they have already been adopted by others.

In other words, the bandwagon effect means that it’s more likely for someone to do, say, believe something if a high number of other people have already done so. This is sometimes also called groupthink or herd behavior.

Brian: In social media, I’ve seen this happen before where maybe a new social network opens up and then it feels like everyone, celebrities, other marketers, friends, are all joining up so you end up joining, too.

It’s pretty easy to imagine how helpful this can be with marketing. If it feels to a new user like everyone loves your product then they’re more likely to love your product too.

Some of the ways we can work to use this perception to our advantage are for example testimonials. If you have a lot of testimonials it might feel like everyone loves your product, company, or business.

Hailley: Exactly. And it’s interesting because I really think user-generated content can help a lot here if you share photos on Instagram of all the other customers enjoying your product for example.

And even influencer marketing can add to this effect if it starts to feel like a lot of influencers love your product then people are more likely to jump on the bandwagon.

Ultimately, this cognitive bias is all about critical mass so you have to have the numbers for this feeling to really take root.

2. Zero Risk Bias or the Certainty Effect

Brian: Next up let’s chat about zero risk bias or the certainty effect. It is exactly what it sounds like. Essentially our minds have a tendency to favor paths that seem to have no risks, they are certain.

This is why you see many brands and businesses offer money-back guarantees and risk-free trial offers. This feeling of zero-risk is really appealing to customers especially when it’s a new product or service that they’re experiencing.

Here, the more you can reassure customers and potential customers of limited risks, the more they are likely to feel better about their decision and that decision will even come to them more easily.

Hailley: I’ve seen this done in a lot of great Instagram video ads recently where on top of showing the product there’s always text that says “money back guarantee” or “risk-free.” Or in social media posts with a photo of your product, you can also play to the certainty effect in your text as well.

While it’s easy to use this one on your website for the copy it’s also definitely possible to use it in your advertising and social posts as well and that will really help leverage the zero risk message to your advantage.

3. In-Group Favoritism

The next bias on our list is in-group favoritism. This means that people prioritize products and ideas that are popular with a group they’ve aligned themselves with.

Brian: It really makes you realize how powerful your identity is. Let’s say you’ve aligned yourself with a certain sports team, well the data shows that you and everyone else who identify with that sports team are more likely to buy similar products and use similar services. Essentially, you and the others who identify as a group favor specific things.

However, experiments have suggested that group identities are flexible and can change over time so keep that in mind as well.

One really good example of a company leveraging this was Apple. They really built an “us vs. them” mentality among their customers with their marketing campaigns and essentially created their own in-group.

Hailley: What this means in terms of marketing psychology is that if you can find these identity markers and you know what in-groups your customers and potential customers are aligned with you can choose your marketing strategy accordingly.

For example, you can join a bunch of communities where your ideal customer hangs out to learn more about their identifiers or create surveys among your current customers to learn about identifiers. And it might take a bit of work but you can also create your own community and in-group if that’s a good fit for your social media marketing!

Quick shout out, check out www.buffer.com/slack if you want to join our Buffer community on Slack.

4. Confirmation Bias

Brian: Another really popular bias is called confirmation bias.

This is the effect where our mind searches for, interprets, favors, and recalls information that confirms or amplifies beliefs that we already have.

This effect is even stronger when it comes to emotionally charged issues and for deeply entrenched beliefs so in those cases instead of looking for new information people stick to what they already believe.

It’s easier and less work for your brain to stick to your current beliefs than to have to go through the decision-making process and choose a whole new set of beliefs all over again.

Instead, your brain is looking to back up your current beliefs and reassure you that it was a good decision.

It’s interesting to note that even scientists fall prey to this bias, it is very common.

Hailley: When it comes to confirmation bias, it also tends to contribute to overconfidence in personal beliefs.

When it comes to leveraging this type of marketing psychology there are a few things to consider.

First and most importantly you really need to know your audience and what their existing sets of beliefs are. You can do this by checking out a few of them on social media and looking for articles they share or asking them if you’re in touch with customers often. There are quite a few ways!

From there, you can share information from your brand that they already believe to be true. And if you do that well, they’re going to already agree with the content that you’re sharing and they’ll put more trust and belief in your brand as well.

Brian: Another way to incorporate this is to have really detailed product descriptions that assure people of the things they likely already believe about your product or business, maybe related to the quality or customer service, or value.

5. The Endowment Effect

Hailley: We have two more cognitive biases for you today. One is called the endowment effect and this is already quite popular among marketers.

It’s the idea that people assign more value to things merely because they own them.

So as marketers there are a lot of ways to play to this effect. Think of free coupons, free trials, and sample products.

All of those experiences create a sense of ownership that people are less likely to want to give up.

Focusing on marketing psychology, we offer free trials at Buffer and once someone has spent the time importing their social media accounts and incorporating Buffer into their routine they are less likely to want to give up the ownership they feel over their account when the trial ends.

Brian: The strange thing about the endowment effect as well is that people tend to pay more to keep something they already own than to get something new that they do not own — even when there is no cause for attachment, or even if the item was only obtained minutes ago.

If you’re in a mall and you buy an expensive sweater and then go to the store next door and see another nice sweater for even less money, you’re more likely to keep the original expensive sweater because you’ve already claimed ownership over it.

This point in marketing psychology is also related to status quo bias. This bias talks about how people like things to stay the same. So it, sort of, works in tandem with the endowment effect because once you have ownership over something you want that to stay the same, you don’t want to give it up.

6. Not Invented Here

Hailley: Let’s talk about something called “not invented here.”

Not Invented Here is the aversion to use products or accept ideas that are developed outside of a group. As a social phenomenon, this can manifest as an unwillingness to adopt an idea or product because it originates from another culture.

If you as a customer don’t recognize, identify with, or understand a product or service you’re less likely to use it.

Brian: Exactly, and a very common way to overcome this bias is for newer companies to align themselves with well-known brands in content partnerships or swaps.

We do a lot of those at Buffer and they are effective for so many reasons and it seems like this could be one of them.

Say you’re a product from another part of the world and you’re trying to get U.S. customers, maybe you align yourself with a few U.S. companies on social media to help overcome the “not invented here effect.”

Another common way to use this marketing psychology is to feature the logos of well-known media companies on your website because if someone trusts the opinion of a place like.

FastCompany for example, and you’ve been featured in FastCompany, then that person is more likely to trust your brand because you’re associated with them.

Want more posts like this? We write stories of businesses doing great stuff on social media, the latest social media experiments to try, and news and trends that’ll help you succeed on social media. Subscribe here →

This blog post was first written on the Buffer Social blog on January 21, 2019.

6 Powerful Psychological Biases was originally published in Buffer — Social on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Stories by Buffer on Medium https://medium.com/social-media-tips/6-powerful-psychological-biases-14191a510f33?source=rss-245d5483fb27——2

Apple’s “Shot on iPhone” photo contest sparked some controversy back in January over whether it would pay winning photographers to use their photos in ads, but the company quickly clarified that licensing fees would be paid. Well, the contest has ended, and Apple has just unveiled the winning iPhone photos.

The 10 winning photographers represent countries all around the world and were selected by an international panel of judges: Pete Souza, Austin Mann, Annet de Graaf, Luísa Dörr, Chen Man, Phil Schiller, Kaiann Drance, Brooks Kraft, Sebastien Marineau-Mes, Jon McCormack and Arem Duplessis.

Here are the winners along with explanations by select judges on why the photo was picked:

Chen Man says: “This is a photo filled with lovely color and sense of story in the composition. Zooming in, you can see details of each family and their unique touch. The basketball hoop is placed right in the middle of the photo, adding more stories behind the image.”

Annet de Graaf says: “The narrative in architecture. There is actually life behind the surface of an average apartment building in an unknown city. Vivid colors and a perfect composition with the basketball board right in the middle! Great eye.”

Austin Mann says: “This image took a lot of patience and great timing … with the iPhone’s zero shutter lag and Smart HDR, we’re able to see both the raccoon’s eyes and the deep shadows inside the log … something that would have previously been nearly impossible with natural light.”

Phil Schiller says: “The stolen glance between this raccoon/thief and photographer is priceless, we can imagine that it is saying ‘if you back away slowly no one has to get hurt.’ A nice use of black and white, the focus on the raccoon and the inside of the hollow log provides an organic movement frozen in time.”

Phil Schiller says: “A reflection that looks like a painting, two worlds have collided. You are compelled to think about where and how this photo was taken, the bird flying in the corner provides the single sign of life in an otherwise surreal composition.”

Chen Man says: “Distortion and reflection at a strange angle — this photo creates a fantastic feeling.”

Austin Mann says: “I love how accessible this image is: You don’t have to travel to Iceland to capture something beautiful, it’s right under your nose. The way the lines intersect, the vibrant color, the sense of old and new … this is just a great image.”

Luísa Dörr says: “I like the simplicity of this image, the composition, light, details, everything looks good. Then you see one small line that looks wrong and makes me think what happened, where is this place, who was there. For me a good image is not only one that is strong or beautiful, but makes you think about it — and keep thinking.”

Sebastien Marineau-Mes says: “Love how the heart shaped water puddle frames the subject, capturing a glimpse of the world as the subject hurriedly walks past.”

Brooks Kraft says: “A unique perspective and a new take on the popular subject of shooting reflections. I like that the subject is evident, but you are not really sure how the photo was taken. The puddle is the shape of a heart, with nice symmetry of the subject. The depth of field that iPhone has in regular mode made this image possible, a DSLR would have had a difficult time keeping everything in focus.”

Brooks Kraft says: “A portrait that captures the wonderment of childhood in a beautiful setting. Great composition that shows both the personality of the child and the experience in the surroundings.”

Pete Souza says: “Nice portrait and use of background to provide context. The placement of the child’s face is in an optimal place — lining her up so the background directly behind her is clean and not distracting. The setting is a familiar — I’ve probably stood in this exact spot. But the picture is not like any I’ve seen from this location.”

Jon McCormack says: “This image is very well thought through and executed. The background pattern holds the image together and the repeated smaller versions of that pattern in the water droplets create a lot of visual interest. The creative use of depth of field here is excellent.”

Sebastien Marineau-Mes says: “Very unique composition and color palette, playing to the strengths of iPhone XS. What I find most interesting is the background pattern, uniquely magnified and distorted in every one of the water droplets. I’m drawn to studying and trying to elucidate what that pattern is.”

Kaiann Drance says: “Looks like a simple scene but a good choice of using black and white to elevate it with a different mood. Helps to bring out the dramatic contrast in the clouds and the surrounding landscape.”

Luísa Dörr says: “I feel like this landscape was treated like an old portrait. The texture of the mountains evokes an old wrinkled face. Portraits and landscapes are the oldest way of creative representation by humans. There’s something about it that belongs to the realms of the subconscious mind, and this is mainly what appeals me of this picture; the part that I’m not able to explain.”

Kaiann Drance says: “Gorgeous dynamic range. There’s detail throughout the photo in the meadow, trees, and clouds. Beautiful deep sky and pleasing color overall.”

These 10 winning photos will now be featured on Apple’s billboards (in select cities), stores, and website.

from Sidebar https://sidebar.io/out?url=https%3A%2F%2Fpetapixel.com%2F2019%2F02%2F26%2Fhere-are-the-winners-of-apples-shot-on-iphone-photo-contest

In 1994, the Italian housewares manufacturer Alessi released Anna, a corkscrew topped with a woman’s smiling face. It was created by Italian architect, artist, and designer Alessandro Mendini and inspired by Mendini’s friend, the designer Anna Gili. As you stab the screw into a cork and twist, Anna’s arms rise up over her head in a silent hallelujah to the wine-fueled revelry that awaits. Today you can buy all manner of wine openers: electric ones, air pressure pumps, one-handed varieties. But how many corkscrews can make you laugh out loud?

Exuberant design was Mendini’s specialty. Mendini died last week, age 87, and his death leaves a void in the school of thought that favored emotion and surprise over the cold efficiency that has come to dominate much of design, calibrated as it is to the precise and bottomless needs of the technology industry.

Mendini was trained as an architect, but he had deep roots in the art world. A postmodernist, he was a central figure in Italy’s Radical Design movement, which sought to imbue art in design, and which served as a precursor to the influential Memphis style that today’s young designers (and many a corporate copycat) have revived and remixed for mainstream consumers. Mendini’s Proust chair, designed in 1978, was a feverish bricolage: an upholstered Baroque armchair splattered in a pointillist painting by the neo-Impressionist Paul Signac, with a name lifted from French literature. An icon of postmodernism, it is now in the permanent collection of the Museum of Modern Art and the V&A in London.

Mendini also worked as a journalist and was editor of the prestigious Italian architecture magazine Domus from 1979 to 1985. But his popular legacy will be most pronounced in the dozens of kitchen products and home decor he developed for Alessi and others. Each is a testament to the idea that design is not merely a vehicle for solving problems; it can be a source of simple pleasure.

Anna, for instance, was so beloved, Mendini reprised her likeness in a champagne cap, a pepper mill, a tea set, a kitchen timer, and a bottle cap. You could give your kitchen’s entire top drawer over to Anna’s goofy grin if you were so inclined. Consider how unusual it was to portray a literal face in industrial design in the 20th century, at a time when Mies van der Rohe’s ubiquitous catchphrase “less is more” represented the peak of taste and sophistication. The tyranny of minimalism continues today. Take the many smartphones and smart speakers that are designed to be so invisible, they’re easy to forget altogether–to the detriment of consumers. Mendini’s work offers a refreshing antidote. His design was never quiet. He didn’t shy away from figurative representation. Another one of his kitchen designs is a parrot-shaped corkscrew, the feathers of which have the same frenzied print of the Proust chair. Open a bottle of wine, and watch the bird flap its dazzling wing.

Even Mendini’s subtler designs felt revelatory. For Kartell, the Italian manufacturer of high-end plastic furniture, he created Roy, a series of side tables that resemble colorful stools from afar. Up close, the resonant patterns of Roy Lichtenstein’s Pop Art appear on the surface. Another kitchen item for Alessi, the Tegamino pan, looks like any other pan. But it has undulating handles that conjure up the gooey textures of a scrambled egg and revel in the pot’s reason for being: to put warm food in your mouth.

It seems like kismet that some of Mendini’s last Alessi designs were for children. The Alessini collection, a set of whimsical plates, bowls, cups, and cutlery, was designed to capture the imagination of the most naturally curious among us. There’s a radical dignity to them, and to all Mendini’s works. They suggest that consumers are worthy of joy and pleasure, that the mundane but crucial rituals in our lives–cooking, drinking, spending time with children–are not merely chores to slog through, but moments to celebrate. We are what we eat, and what we eat it in.

from Fast Company https://www.fastcompany.com/90310020/the-lost-art-of-designing-for-pleasure?partner=feedburner&utm_source=feedburner&utm_medium=feed&utm_campaign=feedburner+fastcompany&utm_content=feedburner

A guide to newcomers.

“You are not your user”
A reminder that you are not designing the product for people like yourself. Often used as a way to encourage more user research in a project.

“If Henry Ford had asked people what they wanted, they would have told him faster horses”
Used as a counter-argument to the previous statement, when you start to realize you won’t have time or money to do enough user research.

“We are testing the design, not your skills”
Disclaimer given to users at the start of a user testing session to make them feel better about being stupid.

“Designers should have a seat at the table”
When you are not able to prove your strategic value to the company based on your everyday actions and behaviors, and you have to beg to be invited to important meetings.

“Choices should be limited to 7 plus or minus 2”
A nicer way of saying that choices should range from 5 to 9, without sounding too broad. When in reality every good designer knows choices should range from 1 to 2.

“People don’t want to buy a quarter-inch drill bit; they want a quarter-​inch hole”
Wait, do they really want a hole? Or do they want wireless Bluetooth instead, so no holes are needed whatsoever?

“UX should be a mindset, not a step in the process”
When you realize the deadline is coming close and you haven’t been able to finish your deliverables. Used to try to retroactively convince the PM to extend the project timeline.

“Content is king”
A pretty strong argument to convince everyone to push the deadline because you haven’t received the content that will go on the page you are designing.

“Never underestimate the stupidity of the user”
An efficient way of outsourcing your own responsibility of giving users enough context so they know what to do (a.k.a. being a good designer).

“I’m wondering if this breaks accessibility standards”
Used as last resort when you are running out of arguments to convince other designers their design is not working.

“A user interface is like a joke; if you have to explain it, it’s not that good”
An easy way of killing that onboarding wizard/walkthrough idea your stakeholders are asking for. Watch out for the backfire: others might agree with your argument and blame on you the fact that the product is not that working that well.

“People don’t scroll”
The most offensive statement you can throw at a designer.

“People are used to scrolling; think about the way you use Instagram”
A polite counter-argument to the previous statement. The Instagram example can be replaced by any other feed-based product your interlocutor might be addicted to.

“The fold doesn’t exist”
If you can’t convince them, confuse them.

“UI vs. UX”
Pzajsodiajhsknfksdjbfsdbfkqwehjoqiwejroe. Usually followed by even more cliché analogies of ketchup bottles or unpaved walkways.

“All pages should be accessible in 3 clicks”
Just. Don’t.

“Should designers code?”
A commonly used wild card when the audience is running out of questions in a Q&A session at a design event.

“If you think good design is expensive, you should look at the cost of bad design”
A passive-aggressive way of explaining to clients you will not reduce your price. Usually ineffective.

“You can’t design an experience; experiences are too subjective to be designed”
An argument used by coworkers who are running out of things to say but somehow still want to sound smart.

“Let the users decide”
None of us is going to win this endless argument, so we should take this to c̶o̶u̶r̶t̶ user testing. But I’m still going to prove you wrong at the end.

“No one enters a website through the homepage anymore”
Popular at the peak of the SEO era (2005–2008), the argument was used to cut short endless meetings where a large group of stakeholders is trying to design your homepage by committee.

“The only other industry who names their customers ‘users’ are drug dealers”
Can’t even explain why this one exists. Used a lot when the term UX came about in the early 2000s, is becoming pretty popular again in the “designing for addition” era.

“When escalators break, they actually become stairs”
Originally used to explain the concept of graceful degradation, the quote started being adopted by developers to convince the product owner that certain bugs do not need to be fixed.

“Mobile users are distracted”
Just a generalization made by someone who still thinks the primary use case for mobile devices is on-the-go, while doing groceries and simultaneously trying to tame a wild giraffe.

“You don’t know what you don’t know”
Honestly, no one knows.

“Leave your ego by the door”
An inspirational quote used before you walk into a user testing session or a collaborative work session with your coworkers. Looks particularly great if written in Helvetica, printed and framed, and hung by the entrance of truly collaborative office spaces.

“Double diamond”
Hey, we need a slide in this deck that represents our design process — can you come up with something that is relatively simple to understand, that will make us look less chaotic than we actually are?

“Users don’t read”
An overly used argument to convince clients and stakeholders to cut copy length in half. If you made this far to this article, you’re living proof that this statement is untrue.

Any cliché missing from the list? Please add it in the comments.

This article is part of Journey: lessons from the amazing journey of being a designer.

A comprehensive (and honest) list of UX clichés was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Stories by Fabricio Teixeira on Medium https://uxdesign.cc/a-comprehensive-and-honest-list-of-ux-clich%C3%A9s-96e2a08fb2e9?source=rss-50e39baefa55——2

Can LSTM + WSAE get us to 82.5% annual returns on the Dow?

Can a Wavelet Transform & Stacked Auto-Encoders help improve a Long Short Term Memory predictor for Equity Indices?

I’ve spent a majority of my adult life in investing.

Recently I became more interested in approaching the topic from a quantitative angle. The promise of automating an investment approach whilst I sit on a beach sipping sangria’s was all too compelling to ignore.

If I have seen further it is by standing on the shoulders of Giants. — Isaac Newton

With Sir Isaac’s expression in my mind I thought what better place to start than existing research papers. I thought hopefully they’ll give me some unique knowledge that I can build up on when I write my own strategies.

How wrong I was.

This is part of a multi-part series, links below:

1. A Financial Blueprint for absolutely everybody
2. What’s all the fuss about Quantitative Trading anyway?
3. Using Artificial Intelligence to generate annual returns of 62.7%
4. Can LSTM + WT + SAE get us to 82.5% annual returns on the Dow?

Around 6 months ago I stumbled across a research paper that on the face of it seemed very promising.

In short the technique goes something like this:

• Start with past prices for equity indices.
• Add some technical analysis.

Very familiar so far, but here’s where it gets a bit fancy.

• Run this data through a Wavelet Transform.
• Then run the output of the WT through stacked auto-encoders.
• And out of this pops the magic.

The predictions are claimed to be more accurate than had you have not done any of the fancy stuff in the middle. And all we’re using is past prices!

Skeptical, I delved deeper.

Wavelet Transform

The paper starts with a 2 level WT applied twice.

def lets_get_wavy(arr):
    level = 2
    haar = pywt.Wavelet("haar")
    coeffs = pywt.wavedec(arr, haar, level=level, mode="per")
    recomposed_return = pywt.waverec(coeffs, haar)
    sigma = mad(coeffs[-1],center=0)
    uthresh = sigma*np.sqrt(2*np.log(len(arr)))
    coeffs[1:] = ( pywt.threshold( i, value=uthresh, mode="soft" ) for i in coeffs[1:] )
    y = pywt.waverec(coeffs, haar, mode="per" )
    return y

Now there isn’t really a clear mention in the paper as to if a wavelet transform is applied to just the close price, or to every input time series separately. They use the phrase “multivariate denoising using wavelet” which I’d assume to mean it was applied to every time series. To be safe I tried both methods.

Thankfully the issue starts to become quite apparent from here.

I’m sure you’ve heard many times that whenever you’re normalising a time series for a ML model to fit your normaliser on the train set first then apply it to the test set. The reason is quite simple, our ML model behaves like a mean reverter so if we normalise our entire dataset in one go we’re basically giving our model the mean value it needs to revert to. I’ll give you a little clue, if we knew the future mean value for a time series we wouldn’t need machine learning to tell us what trades to do 😉

So back to our wavelet transform. Take a look at this line.

sigma = mad(coeffs[-1],center=0)

So we’re calculating the mean absolute deviation across the noisy coefficient. Then..

(pywt.threshold( i, value=uthresh, mode="soft") for i in coeffs[1:])

We’re thresholding the entire time series with uthresh derived from our sigma value.

Notice something a little bit wrong with this?

It’s basically the exact same issue as normalising your train and test set in one go. You’re leaking future information into each time step and not even in a small way. In fact you can run a little experiment yourself; the higher a level wavelet transform you apply, miraculously the more “accurate” your ML model’s output becomes.

Using a basic LSTM classification model without WT will get you directional accuracy numbers just over 50%, but applying a WT across the whole time series will erroneously give you accuracy numbers in the mid to high 60’s.

I thought perhaps I’ve misinterpreted the paper. Perhaps what they did was apply the WT across each time step before feeding data into the LSTM.

So, I tried that.

Yep, accuracy dips below 50%.

We don’t even need to go as far as the auto-encoder part to figure out a pretty huge mistake that’s been made here.

We’re here though so we might as well finish up to be sure.

Stacked Auto Encoders

Stacked auto-encoders are intended to “denoise” our data with a higher level representation. The number of output nodes you give each level will force our data into a less dimensions, with some loss. I’m not entirely sure how this would ever help our LSTM make predictions; ultimately all we’re really doing here is just removing more data that might have been useful to uncover patterns.

Your results won’t vary much here if you roll with stacked auto-encoder’s with greedy layer-wise training or just multi-layer auto-encoder’s.

Going down the stacked auto-encoder route you can build each layer up like so:

def build_model(self,input_shape):
        input_layer   = Input(shape=(1, input_shape))
        encoded_layer = Dense(self.encoding_shape, 
            activation="relu", 
            activity_regularizer=regularizers.l2(0))(input_layer)
        encoded_layer_bn = BatchNormalization()(encoded_layer)
        output_layer  = Dense(input_shape, 
            activation="sigmoid", 
            activity_regularizer=regularizers.l2(0))(encoded_layer_bn)
        self.autoencoder = Model(inputs=input_layer, 
            outputs=output_layer)
        self.encoder = Model(input_layer, 
            encoded_layer_bn)
        self.autoencoder.compile(loss="mean_squared_error", 
            optimizer="adam")

Then just fit and predict layer by layer until you’re 5 layers deep like the paper suggests.

The accuracy suffers a bit when using our leaked WT as input but is still erroneously much better than using LSTM’s alone, which would explain why the paper demonstrated such good results.

The end lesson here is clear, much like Frankenstein’s monster, piecing together random pages from a stats text book isn’t going to help us when we’re still only passing in past price data. The old adage comes true once more — if it looks too good to be true, it probably is.

Disclaimer
This doesn’t constitute as investment advice. Seek advice from an authorised financial advisor before making any investments. Past performance is not indicative of future returns.

Can LSTM + WT + SAE get us to 82.5% annual returns on the Dow? was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Hacker Noon https://hackernoon.com/can-lstm-wt-sae-get-us-to-82-5-annual-returns-on-the-dow-89cd7637eb65?source=rss—-3a8144eabfe3—4

New advancements in technology led to the widespread use of facial recognition technology. More and more business and organizations are using technology because it is fast and requires minimal interaction from the user. It is primarily used for security purposes. Many businesses and homeowners who use this technology do so in an attempt to ensure that only authorized persons can enter specific areas or access certain devices.

But you’d be surprised to know about some of this technology’s many other uses. Here are some you most probably didn’t know existed.

Finding Missing Persons

Facial recognition systems use a database to add photos of missing persons. The system alerts law enforcers when there is a possible match

Early Threat Detection

US Customs officials use facial recognition technology to check the validity of passports. The FBI has also a system in place to check for persons of interest.

There are police officers in the US who can use their mobile phones to check the identities of suspects. They can use the photos to cross-reference with a database of known criminals.

Automated Jaywalking Fines

In China, police wear glasses equipped with Face Recognition technology in public places. Jaywalkers are automatically fined and notified through SMS when identified by Face recognition-equipped surveillance technology.

Book Lending/Libraries

One company has created a book lending library system with built-in face recognition. The system scans the employee holding the borrowed book and updates its database. The same solution could be used in public or school libraries to make the borrowing process faster and more exciting.

Banking with Confidence

Facial recognition is an added layer of security for ATM transactions.

This is to verify the identity of the cardholder.

ATM’s with face recognition capabilities were recently introduced in Spain and in China. These allow customers to withdraw cash from their accounts without using a bank card. Only time will tell if facial scans will soon replace the use of ATM cards.

Casinos Catching Cheaters in the Act

Casinos use facial recognition to spot potential cheaters in their establishments. They also use the system to keep track of blacklisted gamblers. This security measure makes card counting even more difficult.

Sporting and Entertainment Events

Facial recognition technology has seen a rising demand in concerts and sporting arenas. This is due in part to the increasing number of terror attacks in public events. Facial recognition systems can check if an audience member has any criminal record.

Several venues use the system information to offer better deals to frequent patrons.

Famous people in attendance are also detected using the technology.

Online Purchases with Selfies

Face recognition authenticates the identity of people making online purchases using their smartphones. The system confirms the transaction upon verifying the buyer’s information in its database.

Retail Outlets

Some retail stores began using facial recognition technology in their services. Customers can pay for their merchandise by having their faces scanned. The system then links the photo to the customer’s account in their database. Some stores also use this technology to detect shoplifters. The system can also detect persons who have displayed unwanted behavior in the past.

Bar Fines

There are several bars that began using face recognition software. This allows them to detect teenagers who use fake IDs to buy alcohol and cigarettes. This discourages underage drinking and use of illegal substances.

Restaurants

A KFC branch in China uses face recognition technology to accept payments. Customers pay by smiling at a screen equipped with the technology. The system checks its database and asks for a phone number for an added security check.

Hospitals

A hospital in China uses face recognition to accept payment for medical bills. Individuals need to create an initial profile for the system to use.

Schools

There are schools in the UK which have adopted face recognition to track attendance. The technology is also used to ensure order in classrooms. The system can also detect outsiders who pose as potential threats to the school. This may prevent gun attacks in the future.

Smart Cars

The new wave of automobiles now come equipped with face recognition software. This allows the vehicle to start only upon recognizing the driver. The system includes the safety feature of checking the driver’s level of alertness.

Hotel Booking

There are already hotels that use face recognition to scan returning guests. The system retrieves the guests’ profile and preferences. This allows the hotel personnel to give the guest a more personalized and warm greeting.

Select hotels have booths with face recognition technology. These kiosks issue the key card for the room to the guests themselves.

Dating Sites

Finding one’s potential mate online has become popular because of social media. Some dating sites now offer face recognition to match people with similar attributes. Although there are complaints of non-human matches reported in some sites:)

Airport Boarding

Several airports have introduced facial recognition in their operations. This makes the checking in of luggage and the boarding process more convenient.

The system uses the passenger’s passport photo to verify against a database. Passengers are only allowed to check-in and board when there is a match in the system.

Identifying Pets!

Face recognition is also used in identifying the breed of your pet. This capability makes it possible to find missing pets as well.

In conclusion

Face recognition is far from perfect. Different facial expressions and lighting conditions may affect the software’s accuracy.

The technology only works well when the subject faces forward. Objects that are too far away may also become hard for the system to recognize. But advancements in AI and facial recognition keep on evolving. These developments will address the inaccuracies.

More and more applications are developed for facial recognition. The choices are limited only by your imagination and the technology’s increasing capabilities.

18 Surprising Uses of Facial Recognition You Didn’t Know Existed was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Hacker Noon https://hackernoon.com/18-surprising-uses-of-facial-recognition-you-didnt-know-existed-af18244ac88?source=rss—-3a8144eabfe3—4

Chatbots are conversational interfaces meant to assist individuals in interacting with larger organizations.

If you’re looking for help while browsing a website, you might end up texting with a chatbot. They’re a bit more dynamic a problem-solver than a simple FAQ webpage. If you’re looking to speak with a customer service representative, a chatbot might try to help you out on its own, or it could help direct you to the proper department within an organization, where a human operator can provide assistance.

As this technology becomes more and more integrated into daily commerce, it’s worth asking: what barriers does the technology face today, what improvements are being made, and what can we expect of human-robot discourse in the future?

Limitations

It won’t take you all too long, under a certain line of questioning, to reach the limitations of bot technology in 2019.

As a four-time winner of the Loebner Prize — an annual competition that judges the world’s most human-like chatbot — Mitsuku is one of the most advanced algorithms in the field. I decided to have a little chat with her.

MITSUKU: Hi, I’m the world’s most humanlike conversational AI.

ME: Hi Mitsuku, can I interview you for an article about chatbots?

MITSUKU: Hi there unknown. Why do you want to do interview me for an article about chatbots so much?

ME: “So much” may be a bit strong. I figure you’d be able to help me understand chatbots better, yourself being one of them.

MITSUKU: Here’s something I meant to show you. Enter a date and I will tell you what the UK number one hit single was on that day.

MITSUKU: Please explain your reasoning.

As I spoke with Mitsuku, it became evident that she and I had a somewhat different sense of how our conversation was going to go.

ME: Apologies Mitsuku, but could we stick to the topic of conversation? I’m not interested in discussing music with you, so much as the intricacies of AI-integrated conversational bots in 2019. Does that make sense?

MITSUKU: No need to apologise. Do you mind if I tell other people?

MITSUKU: What are you interested in?

MITSUKU: It makes sense to me.

Notice how Mitsuku picks up on particular terms I use: “apologise”, “interested in” and “makes sense”. Mitsuku may be award-winning, but in her online form, she’s reminiscent even of the earliest chatbot technologies.

ELIZA was perhaps the earliest successful chatbot, created by an MIT researcher. Developed only a decade and a half after Alan Turing first proposed his so-called “Turing Test”, Eliza was able to hold full conversations (in a sense) with humans who typed to her.

ELIZA was, technically, quite complicated — more than can be explained simply in the scope of this article. Overall, though, her gimmick was pretty straightforward: whatever you’d type, she’d parry back in the form of a question.

“Men are all alike,” you might say.

It replies back: “what is the connection, do you suppose?”

You see? “Alike” to “connection”. Sometimes the mechanism was even more obvious than this.

“Well, my boyfriend made me come here.”

“Is it important to you that your boyfriend made you come here?”

Latest Advancements

ELIZA may be over half a century old now, but her essential underlying mechanisms remain. The majority of chatbots today leverage keywords and phrases in your text, in order to return a response that, even if it does sound like stilted conversation, makes some sort of cogent sense.

The most advanced AI chatbots incorporate similar mechanism but are powered by larger datasets for training and more advanced machine learning algorithms. In addition, today’s chatbots improve by leveraging user input as training data. An AI chatbot can learn to better serve those they converse with over time, by updating its machine learning models as it completes more and more conversations with humans.

In fact, the technology has been developing, in a meaningful sense, on two fronts: in AI development, but also in commerce.

Companies incorporating this mechanism into their customer service models have introduced a suite of new user experience components to the traditional chatbot paradigm.

Pop-up windows during online browsing and shopping are a time-tested marketing tactic, but new bots feature improved interface design, too. One popular trend is to display chats as text bubbles, to capitalize on our familiarity with those visual cues. After all, when the goal is to design a robot that mimics a human in conversation, familiarity in visual presentation is an integral part of the overall experience.

A good example of where commerce and technology come together came in 2016, when CoverGirl released a chatbot version of a social media influencer, Kalani Hilliker.

Studies show that chatbot users skew young, and female, so CoverGirl’s bot acted as a way for that demographic in particular to interact with its brand, in a way that may have felt novel or even fun to some.

What to Expect

We can expect chatbots to improve markedly over the coming years, as natural language processing in AI improves and proliferates.

Major tech companies now regularly build deep neural network technology that allows speech recognition machines to better understand and interact with people.

Once deep neural networks become more common, chatbots will start surpassing the classic Turing Test, proliferating online, and becoming a less awkward part of our everyday lives. In fact, some experts claim that chatbots could, one day, evolve to replace search bars in our internet browsing.

Think about it: when you query a search engine, you’re essentially asking a question that a machine is answering. Most of the time you type in an approximation or abstraction of your question, and then you have to scroll through varyingly relevant results. If that machine were conversational, you’d have a much easier time reaching the search result you’re looking for.

Until that day, though, we’ll have to deal with what we’ve got.

ME: What other people do you know?

MITSUKU: I know far too many people to name them all.

ME: Can you name one?

MITSUKU: Mike.

ME: Ah, Mike! He and I go way back.

The State of Chatbots in 2019 was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.

from Hacker Noon https://hackernoon.com/the-state-of-chatbots-in-2019-d97f85f2294b?source=rss—-3a8144eabfe3—4

Role-based Personas

Role-based personas derive from quantitative data and resemble the customer segments from more traditional market-research practices. Many usability professionals feel that these lack substantive information on the user’s thought processes and are overly reliant on modeling demographics on top of analytics. Personas are capable of answering what questions—for example: “What potentially meaningful patterns exist among our user population?” However, they fail to pursue the why questions. Just because we understand that a pattern actually exists, that does not necessarily mean we understood why it exists. While some UX professionals are comfortable speaking to why answers that are based solely on quantitative data, they typically frame these interpretations as being nothing more than reasonable, logical conclusions. However, these findings are vulnerable to confirmation bias because the conclusions depend so heavily on the researcher’s speculations.

Fictional Personas, or Proto-Personas

Fictional personas, or proto-personas, are based not on user research or feedback, but on the assumptions, anecdotes, and other past experiences of stakeholders and team members regarding users’ needs. While most UX professionals consider such personas to be flawed, the extent of their flaws and the purposes for which you could reasonably use them are less clear. Some UX professionals think teams should create proto-personas only as an exercise to promote thinking about the user as an individual who is different from oneself. Others think they can provide a useful starting point for generating hypotheses about users’ motivations and inclinations, which they would then validate through empirical observation. Critics think the act of creating proto-personas and the time and effort teams put into fleshing them out during such an exercise is toxic. That it creates an inescapable well of groupthink and results in other forms of bias that prevent related empirical-research efforts from moving forward.

Goal-Directed Personas

Goal-directed personas are most similar to the JTBD perspective. Focusing on the users’ goals is semantically similar to focusing on desired outcomes. However, despite their being goal directed, the personas themselves typically remain focused on creating a model of the users’ attributes. The goals typically diminish in importance because most of the researchers’ energy becomes entrenched in elaborating on the persona’s distinct motivations and needs. In the parlance of JTBD, focusing on job drivers, while giving little thought to success criteria stunts the persona’s growth.

A significant part of this fixation on attributes results from the focus on adding vivid details and stories to personas, which tend to be elaborate depictions of full personalities, having complete experiences, in the hope of making the personas feel real and relatable. The desire for personas to feel real and relatable is understandable and even admirable. Unfortunately, attempting to create that sense of reality by making personas feel like real people inherently turns them into something they were never meant to be. As I stated earlier, personas are aggregated proxies for groups of real-world users that represent their mental states and behavioral inclinations within a specific setting. Personas represent interconnected themes that occur within some meaningful, shared timeframe. Treating them as anything more becomes dangerously reductive. Personas do not have full personalities. Nor can they have complete experiences. Only people have full personalities and complete experiences. Personas are not people.

False Assumptions About Categorical Exclusivity

One of the most trying aspects of using personas in the workplace is having a stakeholder or team member walk up to you, with a part of the designed experience in hand, and asking, “Which personas are hitting this touchpoint?” or “Which personas are of critical concern during this part of the experience?” These kinds of questions are frustrating because personas don’t experience our products. People do.

Personas are neat, simple constructions that we pin to our walls. A Pragmatic Patrick is not a Dreaming Daniel and is never going to be. People are messy. They are fluid and fuzzy. A person can start a research and shopping journey as a Dreaming Daniel, then become more like a Pragmatic Patrick as they approach a purchase decision. They can be partly one persona and partly another. Such overlapping states could happen either sequentially or concurrently. The value of personas does not come from asking who is doing what, because a persona is not a who. Their value comes from knowing what mental states and behavioral patterns exist and considering them in all their potential forms. Different users can strongly manifest different personas at different moments, requiring different interpretations of their experience from the same stimulus on subsequent exposures.

I’ll illustrate this with an example: Recently, I was engaged in a research effort. We knew that two similar behavioral patterns existed that led to similar outcomes—at least from our perspective. We needed to learn whether consumers perceived these patterns and outcomes to be similar as well. Could the same workflow satisfy both behavioral patterns A and behavioral patterns B, or were these sets of behaviors sufficiently different from the consumer’s viewpoint to necessitate two semantically discrete pathways?

We found that consumers perceived the two sets of behaviors as being meaningfully distinct from one another and required different pathways. However, their following one pathway versus the other could be extremely variable. What’s more, many of the touchpoints along these discrete pathways either crossed over one another—creating a shared moment on the two pathways—or required some similar mimicry of comparable, parallel moments across the two pathways. Explicit signposts would be necessary to suggest how you might tailor actions along these touchpoints to either A or B behavioral inclinations and motivations, as shown Figure 1.

Our findings were admittedly complex. In our early drafts of the results, it became clear that one specific issue was making it more difficult for our stakeholders and team members to comfortably come to terms with the learnings. They kept talking about A Shoppers versus B Shoppers. With all the discussion of two comparable sets of behaviors and two comparable pathways, the team felt the need to understand consumers moving down those pathways as two categorically distinctive groups of people. However, this rigidity and perceived categorical exclusivity made it more difficult for the team to understand the complex, interwoven relationships between the two pathways. These were not two roller coasters on entirely separate tracks that were simply near one another. They were two different trains that occasionally shared the same track, with railway switches separating and crossing their pathways at various decision points. To understand the true relationships between these two pathways and the two sets of behavioral inclinations, we needed to understand that the actual people moving through these pathways were dynamically more complex than passive passengers on a static course.

Imagine a spectrum with Shopping Behavior A on the left and Shopping Behavior B on the right, as shown in Figure 2. There were research participants who demonstrated strong tendencies toward the two extremes, and there were participants who demonstrated tendencies toward the middle ground between them.

Individuals at the two extremes were categorically dissimilar from each other. They wanted different things and were going about getting them in different ways, as shown in Figure 3. Whatever combination of individual and situational variables added up to their cumulative past experience made them less likely to deviate from those extremes.

Individuals nearer the midpoint between the two extremes were more complicated. Sometimes they wanted different things and were going about getting them in similar ways. Sometimes they wanted similar things, but went about getting them in different ways, as shown in Figure 4. The individual and situational variables adding up to their cumulative past experience had less of a polarizing effect in comparison to that of the participants at the extremes. Instigating movement in one direction or the other was easier and far more likely.

 Any given person has the equivalent potential of behaving either like a category member or a fluid, dynamic point, shifting along a spectrum. Thus, a population inherently does both at the same time. This idea might seem painfully obtuse and academic at first glance. However, a compelling simplicity is inherent in this idea that has huge tactical value—similar to learning that light behaves both like a particle and wave. When working with light, we have to make decisions considering all of the rules that apply, at all times.

Our stakeholders and team members needed to acknowledge that, to create an experience that complemented both of these pathways, the pathways had to be functional for both categorically static and spectrally fluid individuals simultaneously. To make that possible, we needed to create effective signposts that communicated accurate expectations, successfully execute our progressive-disclosure strategies, and follow other UX-design best practices. The only prediction we could reasonably make was that these meaningfully recurring patterns exist and, in most instances, we needed to attend consistently to both of them throughout the designed experience. Moments of addressing one pattern singularly and ignoring the other would be exceedingly rare—if they existed at all.

The sort of dilemma I’ve described is an inherent difficulty from which all personas suffer. Personas represent clusters of thought processes and behavioral inclinations co-occurring in the same moments of time, and we should almost always treat them as though they are simultaneously categorical and spectral. Any given data point that represents a person within an experience could actually be straddling two different personas—emphasizing or de-emphasizing each of them in turn or entering or exiting them entirely.

JTBD is better at fostering this understanding because it does not attempt to differentiate patterns that are based on people. This approach makes no claims regarding who might be thinking this thought versus that thought, nor who might be taking this action versus that action. It simply recognizes that certain thoughts and actions exist, in various forms, and that we need to attend to all of them because they all influence the user’s perception of progress toward the desired outcomes. The only reason ever to differentiate any of these patterns from one another in JTBD is when there is a dichotomy that is based on separate desired outcomes—not different users.

Conclusion

In summary, the attraction of using personas is undoubtedly powerful. The level of understanding that they seemingly promise is seductive. However, by their very nature, personas are exceedingly difficult to create and just as difficult to use. The construction and creation of a set of personas lacks a foundational methodology that has clear, operational definitions.

The primary focus of personas is summarizing complex, abstract concepts by meaningfully defining and clustering attributes that correspond to particular behavioral patterns. However, this focus on clustered themes that center on a representational archetype fosters a false expectation of true categorical consistency with real populations. Using personas is inherently risky because it increases the likelihood that reductive, simplistic thinking and assumptions might dictate design decision making.

JTBD offers a simpler solution that focuses on concrete concepts such as success criteria and avoids the unnecessary complexity of trying to model converging themes of thoughts and behaviors for anything beyond the user’s explicitly desired goals. 

References

Block, Melissa. “How the Myers-Briggs Personality Test Began in a Mother’s Living Room Lab.” NPR, September 22, 2018. Retrieved February 12, 2019.

Boyle, Gregory J.“Myers-Briggs Type Indicator (MBTI): Some Psychometric Limitations.” (PDF) Bond University, March 1, 1995. Retrieved February 12, 2019.

Dam, Rikke, and Teo Siang. “Personas: A Simple Introduction.” Interaction Design Foundation, January 2019. Retrieved February 12, 2019.

Emre, Merve. The Personality Brokers: The Strange History of Myers-Briggs and the Birth of Personality Testing. New York: Doubleday, 2018.

Flaherty, Kim. “Why Personas Fail.” Nielsen Norman Group, January 28, 2018. Retrieved February 12, 2019.

Grant, Adam. “Goodbye to MBTI, the Fad That Won’t Die.” Psychology Today, September 18, 2013. Retrieved February 12, 2019.

Mulder, Steve, and Ziv Yaar. The User Is Always Right: A Practical Guide to Creating and Using Personas for the Web. Berkeley, CA: New Riders Press, 2006.

Nielsen, Lene. “Personas.” The Encyclopedia of Human-Computer Interaction, Second ed. Interaction Design Foundation, 2013. Retrieved February 12, 2019.

Reynierse, James H. “The Case Against Type Dynamics.” (PDF) Journal of Psychological Type, January 2009. Retrieved February 12, 2019.

Spence, Janet T., Robert L. Helmreich, and Robert S. Pred. “Impatience Versus Achievement Strivings in the Type A Pattern: Differential Effects on Students’ Health and Academic Achievement.” Journal of Applied Psychology, December 1987.

Wilkie, Dana. “How Reliable Are Personality Tests?” Society for Human Resource Management, September 11, 2013. Retrieved February 12, 2019.

from UXmatters https://www.uxmatters.com/mt/archives/2019/02/the-pitfalls-of-personas-and-advantages-of-jobs-to-be-done.php

Table 1 shows the types of research questions you might want to explore during individual user interviews, as well as the types of architectural decisions you might make based on the answers.

Table 1—Deep-linking research questions and associated decisions

Research Questions	Decisions They Support
Must users leave one digital tool and go to another tool to complete a task? What tasks require such a cross-application workflow?	Identifying applications and associated workflows that might require deep linking
Is a transition’s sequence always the same? For example, do users always go from Application A to Application B, not B to A?	Identifying the originating application and the destination application and determining whether deep links must be bidirectional or unidirectional.
What are the reasons users must transition from one application to another?	Evaluating whether a deep link is the most appropriate solution
When users leave one tool and go to another tool, do they close the originating application or keep it open to return to it later? If users have both applications open at once, are they on different monitors, in different windows, on different tabs, or in different Web browsers? Do users need to view the applications side by side?	Determining whether the destination application should open in the same tab or a new tab
If users view applications side by side, what is the purpose of doing so? What kind of work is the user doing? Copying and pasting data from one application to another? Verifying data entry? Looking up information in the destination application to evaluate or contextualize the information in the originating application?	Evaluating whether a deep link is the most appropriate solution

You should gather most of this information by having users walk through their process so you can directly observe their behaviors rather than merely relying on their verbal accounts of how they accomplish their tasks.

Once you’ve observed users going through their cross-application workflows and asked the appropriate follow-up questions to understand their needs in greater detail, you’ll be ready to review your data to determine whether patterns exist that tend to describe the reasons users require cross-application links.

Deep-Linking Patterns

When conducting user interviews that focus on cross-application workflows, you’ll likely hear a variety of reasons why users might need to transition from one application to another. In conducting my own research, I have discovered that there are three primary patterns that describe users’ reasons for making transitions from one application to another. Understanding the need for a transition between applications is critical because it might reveal that a deep link is not the most appropriate solution for addressing a user need. It is important to know when a solution would not be the appropriate solution. Figure 2 illustrates the three primary deep-linking patterns I’ve observed in my research.

These three deep-linking patterns articulate distinct user needs for a deep link and, in some cases, might indicate that there is a better solution for addressing the need than a deep link. In such cases, a deep link would be an intermediate step on the path toward creating a more meaningfully integrated set of applications. Let’s dive into each of these deep-linking patterns individually.

Pattern 1

In Pattern 1, users transition bidirectionally between Application A and Application B to compare the same data in two different applications. They might simply need to verify that the data are accurate and consistent, so the directionality of such cross-application inspections might not be important to them. While a deep link would let users go directly from a section in one application to a relevant section in another application—without locating a bookmark for the destination application or laboriously typing in a Web address to view the relevant page—a deep link would be only an intermediate solution in this case.

The real issue is the distinct, underlying databases that support the two applications. Users pay the price for this lack of integration by manually inspecting, editing, and updating records in both applications. The solution should be a single backend system that supports a shared customer record. (See Platform UX, Part I for a description of the user experience of shared data.) One important benefit of exploring user needs around cross-application workflows is the ability to reveal opportunities for changing the platform or architecture of a backend system to better align with user needs. In this example, it is clear that a deep link would be a stop-gap measure on the way to true integration by sharing data on the backend.

Pattern 2

In Pattern 2, users express a need to go from information in Application A to an associated action in Application B. This need is common in situations where users rely on reports or dashboards to make decisions that result in actions. Possible actions might include increasing the budget for a particular service, launching a specific type of advertising effort, or selecting particular types of inventory for discounting.

In this case, situating the information in close proximity to the point where the user decides to take an action helps facilitate the user’s work. Users benefit enormously when their decision-making context provides the relevant information. In this case, the deep link is unidirectional because the user has expressed a need to evaluate information before going to Application B where this evaluation would culminate in an action. A truly integrated platform would dispense with the deep link completely and, instead, would provide recommendations on what actions to take based on the information in a report or on a dashboard. However, a deep link would be an acceptable intermediate strategy along the path toward integration.

Pattern 3

In Pattern 3, transitions between Application A and Application B let users more easily view two different types of data. Such transitions support their gaining a more comprehensive view of the available information by connecting two different types of data that are necessary to convey a full story to the user. While deep links would be an acceptable intermediate strategy for providing the appropriate context to users, an advanced integration strategy would more meaningfully combine those two different types of data into an integrated report or dashboard that would let users view the data in a single space.

Putting It All Together

Now that you’ve gathered user needs and identified patterns across your sample of users, construct a table similar to Table 2 to share this data with your stakeholders. Your table should describe the originating application, destination application, whether deep links should be bidirectional or unidirectional, whether the destination application should open in a new tab or the same tab, the reason users need a deep link, and how you plan to measure the success of your integration efforts. Table 2 shows the information you should give to your team to influence the architecture of deep links.

Table 2—An integration roadmap for deep linking

Originating Application	Destination Application	Directionality	Application Opens In	Deep-Link Rationale	Success Criteria
Application A	Application B	Unidirectional	New tab	Explicitly linking information to an associated action	Quantitative: X% of active users are engaging with deep linking. Qualitative: Users no longer rely on bookmarks or typing Web addresses to make cross-application transitions.
Application C	Application D	Bidirectional	Same tab	Explicitly linking two different types of data to get a complete story	Quantitative: X% of active users are engaging with deep linking. Qualitative: Users no longer rely on bookmarks or typing Web addresses to make cross-application transitions.

Your roadmap should explicitly articulate deep-link mechanics such as directionality, the rationale for having a deep link, and the success criteria for each deep-link candidate. Mapping these elements is necessary to ensure that you fully support user needs and the solution aligns with the problem you’re solving.

While deep links might be just an intermediate step on the path toward more meaningful integration, providing them can have a profound impact on the experience your users have with your software. Deep links communicate that your organization recognizes and responds to user needs. Deep links can be a component of your integration strategy. When you create them with the right intent, they can facilitate cross-application work by providing context and continuity to your users. 

from UXmatters https://www.uxmatters.com/mt/archives/2019/02/platform-ux-facilitating-cross-application-workflows-with-deep-linking.php